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Engineering and Applied Sciences 2020; 5(2): 34-40
http://www.sciencepublishinggroup.com/j/eas
doi: 10.11648/j.eas.20200502.11
ISSN: 2575-2022 (Print); ISSN: 2575-1468 (Online)
Algorithms of Cause-and-effect Approach to Increase Service Net Efficiency
Alexey Anatolievitch Bezrodny1, Anatoliy Mikchailovitch Korolenok
1,
Aleksandr Fedorovitch Rezchikov2
1Faculty of Design, Construction and Exploitation of Pipeline Transport Systems, Gubkin University, Moscow, Russia 2Institute of Control Sciences Named After V.A. Trapeznikov, Moscow, Russia
Email address:
To cite this article: Alexey Anatolievitch Bezrodny, Anatoliy Mikchailovitch Korolenok, Aleksandr Fedorovitch Rezchikov. Algorithms of Cause-and-effect
Approach to Increase Service Net Efficiency. Engineering and Applied Sciences. Vol. 5, No. 2, 2020, pp. 34-40.
doi: 10.11648/j.eas.20200502.11
Received: July 22, 2019; Accepted: August 29, 2019; Published: March 23, 2020
Abstract: Service nets distribute goods and services that is why their improvement is one of the important tasks of any
production chain. There is many models related to the sphere however, in many of them it is possible to see some weaknesses.
At the same time since the task mentioned is complicated and large scaled some systematical approach should be applied that
needs to be modified taking into consideration presence in the systems objects, processes, events and phenomena of various
nature and origin. As possible approach one consideres so called cause-and-effect one that provides a universal description of
complex systems and possibility of descision making in undefined or under-defined situations. In the artcile below this
approach is considered and informational logic diagrams and algorithms to increase service net efficiency are presented. Gas
stations were taken as examples and sphere of practical application, that results are discussed.
Keywords: Algorithm, Complex System, Service Net, Causal-and-effect, Petroleum
1. Introduction
Service nets are important parts of present economy [1].
The statement is true for petroleum supply also [2, 3] that
may be considered as a good example. To develop it there
should be improvements of the existing models and
optimization methods [4-6] to make management and control
more efficient considering last as one of the main tasks of the
present century [7].
A Service net (company, structure, etc.) as any complex
system works under requirements of the upper, goal-orienting
and parents systems, follows demands of consumers, takes
into consideration possibilities of competitors and suppliers,
fulfills laws, makes innovations and so on. These demands,
influences and restrictions (further-factors) on/to/from a
system and its surroundings may be formalized as elements
of mathematical sets that permit to create in a
parameter/factor space some feasible regions, goal areas and
so on.
Under the process approach if one considers gas stations as
an example of service nets it is usually determined the station
themselves (that serve consumers), station complexes or gas
station nets (that provide work of the stations in a region) and
companies as legal entities.
The investigation object is a large scale complex
territorially distributed hierarchical human-machine system
[8] tasks to increase efficiency of which are the ones of many
criteria optimization [9]. Existing models and methods to
resolve modern practical tasks are frequently not enough
since they are not systemized. In addition, there are mainly
considered state-level or object-level structures without due
attention to regional service nets (station complexes),
transportation (clients) flows are modeled as simplistic that is
non-adequate, modern petroleum equipment like OPT
(Outdoor Payment Terminal) and so on is not considered, etc.
The task is to bring efficiency or key performance
indicator (KPI) K to its maximum at given and perspective
factors of the system and surroundings G during ∆t by
developing structures S = X, U,GR and choosing control
actions, C,A,X, U,R that is
35 Alexey Anatolievitch Bezrodny et al.: Algorithms of Cause-and-effect Approach to Increase Service Net Efficiency
( ), , , , , , , maxК S C A X U R t G∆ →
where X – set of control means, U – relation between them,
GR – structure graphs, С – control functions, A – control
algorithms, R – variant of a control structure. In given
formulation the task is not resolved in general because of
large scale, diversity of components and non-linearity of their
interactions that demands a new approach.
2. Method: Causal-and-effect Approach
to Increase Efficiency of Complex
Systems
The most important regularity of complex system behavior
is historicity or development in time [10, 11]. These ideas are
formulated in all fields of knowledge and practical activity
[12]. At the same time relative simplicity and observability of
cause-and-effect (causal) interactions is the reason for their
particular «refusal», since for example from «the earlier» is
not exactly derived «in accordance with/because of». This
situation caused necessity of the following research that is
done in some spheres [13].
In general, it is possible to consider that modern scientific
knowledge is based on the determination of causal
interactions between objects, processes, events and
phenomena (further – objects). They unify visions from
intuitive through scientific to philosophical permitting formal
logical description at deep investigation of the functional
spheres, taking into consideration origin of the events, their
vicinity and development in time and provide matching of
knowledge that comes from various spheres of theory and
practical activity.
Every object of a system, a process realized, event as
changing of state or phenomena of surroundings has its
reason of origin and development that connects them with
other objects. Goals as future states of a system are achieved
at conditions determined by mentioned factors (condition 1).
Results (effects) of causal interaction changes a system and
surroundings that performs some new conditions (or
condition 2). For analytical description the cause-and-effect
cell operation algebra is used similar to finite-state machine
one [6]. The model of the cell (general at the first level of
decomposition) is as follows:
Re /
Kernel
1 2
ason cause Effect
Condition Condition
⟩
To operate with the cells some set of operations ОС
(ос=1.. OC) is formulated:
Unification (system model creation),
Decomposition (structuring),
Intersection and Cartesian products (multi-circuit control
structure),
Complements (extra- and interpolation of parameters
between parts of the system with the most trustable data);
Composition (developing of the whole system model by
following flowcharts of processes of known systems and
models, so-called base or etalon models);
And substitution (synthesis of structures optimal on
criteria).
To increase efficiency the model of an elementary causal
cell structure mentioned was changed. Achieving of a simple
goal by means of elementary control task solution is modeled
by the elementary cause-and-effect cell. Part of the system at
state SA with KPI K to achieve goals at the factors of
surroundings GA under control СА by converting of
resources WA is brought by means of functions and
algorithms, contained in the kernel of a causal cell, to the
state SB with conditions GB and control CB corrected
accordingly to the goal achieving degree ( GA GB− ), new
K* («*» – after interaction) and output resources flow WB. It
is possible to express K, G, S through each others. For non-
elementary cases some cause-and-effect (or reasone-and-
consequence, RC) complexes are created for the part or
whole of the system. Casual components interact accordingly
to the flowcharts of base models using operations of OC set.
There fore new model of RC-cell on the first level of
decomposition at matrix view is presented, where А – before
and B – after interaction:
, *,
( , , )
, , , ,
K СA K СB
A CA GA WA
SA GA WA SB GB WB
The solution of tasks in known situation is achieved by
putting general, known or theoretically and eхperimentally
proved RC-cells, its components decomposition till the level
understandable by decision-makers using base models,
practice performance checking, necessary feedback and
correction.
If there are unknown situations or in case when there is no
enough information about the system, surroundings and their
interaction some parts with the most trustable data are
determined. For them procedures mentioned to known
situations are realized, general RC-models of the whole
system are developed and optimization tasks are resolved
considering known models with accuracy of data available.
Results are step-by-step improved while the system is
developed and/or one gets new data. Information about
results put in some Petrol Data-Base (PDB). Since there are
no restrictions on types of functions and algorithms of
kernels, it is possible to describe interactions of objects of
different nature.
3. Results (Algorithms and Diagrams) to
Improve Service Station Net
Optimal control parameters task causal formulation is
shown as folows:
Engineering and Applied Sciences 2020; 5(2): 34-40 36
( )*
, , int
, , , int
Station
Station
inp out
K G
А К G
G n nα
↑
where ninp – input transport flow, nout – outpuit flow, Guv ∈ G
– factors of the system and surroundings (u=1.. U – type,
v=1.. V – kind), int – quasi-stationary time intervals, where
linear dependence or constancy are adequate and possible, α
– statistical significance, Kstation – efficiency indicator to be
increased, Кstation↑.
Generalized algorithm to determine efficient parameters of
gas stations at given factors of surroundings is presented on
Figure 1, where R – factors/parameter space, R*-feasible
region, X and Y – data of comparable objects, av –average,
∆K – error. «End» operator is dotted since the algorithm is
cycled due to necessity of step-by-step improvement of the
system.
Figure 1. Generalized algorithm to determine parameters of efficient service systems.
The task to synthesize the structure of complex multi-
circuit systems, optimal on Kpetrol at given G, where Kpetrol is
brought to MAX by development of structures and selecting
of control actions at causal formulation is as follows:
( )* * *
1 4
max ,
, , , , ,
petrol petrolK X*,U* К *
A C,H,P,X,U
G C,H,P,X,U w G w GR GR GR
→ −
where w1..6 – resources (1 – staff, 2 – technology, 3 – energy,
4 – knowledge, 5 – finances, 6 – materials), P – set of
processes, GR, GR1 – GR4 – structure graphs of,
correspondingly, infra-system (non-active and needed
control), control (1), decision making (2), organization-
technical (3) and information (4) systems. It is supposed [14]
that the models are enough to describe a whole system. A
system structure is synthesized accordingly to the following
informational logic diagram presented in written form.
On the I-st stage there is an analysis of system at
37 Alexey Anatolievitch Bezrodny et al.: Algorithms of Cause-and-effect Approach to Increase Service Net Efficiency
surrounding conditions.
a) Designation and specification the goals as components
of the X vector given by decision-makers. It depends on
factors of surroundings G, flow chart of processes S,
control means characteristics X and relations between
them U, i.e. X (G, S, X, U). Quantitively they are
determined by data of real working objects.
b) Specifying the system in surrondings adding to it some
controllable components data of which descision-
makers can evaluate goals achievability.
c) Determination of boundaries between controllable and
control systems accordingly to activity (deliberate
changing of information) of components. Non-active or
infra-system does not have the property and needed
control.
d) Determination the factors of Consumers, Suppliers,
Competitors, Macro-economics, upper- and lower-level
systems and so on as G components.
e) Designation Petrol PetrolPetrol
Petrol
R LК
С
−= as KPI, where
RPetrol – results, СPetrol – costs, LPetrol – loses (outages,
untrained staff, etc.), and ∆KPetrol possible.
f) Formation of parameter space and efficient working
areas Еef (see Figure 1).
Figure 2. Typical relations between goals, processes and objects in gas
station nets.
On the II-nd stage the structure of control system is
formed.
a) Formation of the process flow charts and object
structures accordingly to the known models that are
acumulated in PDB.
b) Determination of permitted dominative and sequence
relations between objects N, processes P and goals G
using results of the sphere analisis [15] done (Figure 2).
On Figure 2 P1 – petroleum product supply, P2 –
operational activity, P3-sales and consumer service, P4 –
accounting and reporting, P5 – maintenance and
repairing, P6 – staff training, P7 – security, P8 – energy
provision, P9 – transport, P10 – information service, P11
–purpose-oriented direction, P12 – procurement, P13 –
analysis, P14 – decision making, P15 – control; Xpq –
control means (p=1.. P – type, q=1.. Q – level); N1.. r –
infra-system objects non-active at the view.
c) Formation of controllable system structure as inter-
connected recource-converting objects accordingly to
the processes structure of Figure 2.
On III-rd stage control system structure is formed.
a) Specifing the control time periods Hk (k=1.. K), control
functions Ci (i=1.. I) and control means Xpq accordingly
to the models of PDB mentioned.
b) Forming sets of elementary control tasks :
and circuits
1 5jkpq .. j k pqFC : C P H X× × × where «x» means
Cartesian products.
c) Control system structure model creation or Ω-synthesis
and refusal circuits with the low efficiency, w/o
necessary automation level or meaningless.
On the IV-th stage there is a synthesis of control system
structure by circuits convolutions. Some better Xpq may be
added and convolutions are done until limits of their
properties, considering efficiency and level of automation.
a) C-convolution (synthesis) as integration of control
functions alongside control circuits and designation
more Ci to the smaller number of Xpq.
b) P-convolution as an integration of control functions
belonging to various control circuits of processes Pj by
designating of more Ci to be performed by the same
Xpq.
c) Н-convolution as an integration of control functions Ci
on various time periods by lower number of Xpq.
On the V-th stage one looks optimal variants of the system
structure.
d) Forming of control circuit set (Ω’synthesized) and
determination of КPetrol.
e) Designation as optimal those structures, KPI of which
are closest to Eef. If it is not achieved, one goes to
Stage I.
f) Forming of organization-and-technical system
structures by bringing new and/or improving existing
control means accordingly to the control system
structure (see p. 5.2) and requirements to control means
Xpq.
g) Forming the information system structure by putting
data arrays and transmission channels to control and
organizational-technical system structures already done
(see pp. 5.2 and 5.3).
h) Forming the decision-making structure by designation
to the components of organization-and-technical system
Engineering and Applied Sciences 2020; 5(2): 34-40 38
structures and the same for information system types of
decision-making acts using PDB mentioned [15].
i) Proposal to improve the model and algorithm, transition
to Stage II.
The task to form structures and choose optimal control
actions for service nets using system cause-and-effect
approach is resolved by the following informational logic
diagram (Figure 3).
Figure 3. Information-logic diagram to form structure and choose control actions for service (gas) nets, optimal on the given criteria with using system
reason-and effect approach.
4. Discussion
As a result of the causal approach application proposed it
was developed the complex of inter-related informational
logic diagrams and algorithms to impove service nets as a
part of the methodology of rational development and
continious improvement of service station nets and effecient
automatical control of processes and objects in the systems
(Methodology). Basic components of the methodology are
presented on Figure 4.
On the base of the Methodology there were solved some
practically important tasks [15].
In particular, there were found optimal gas station
parameters at various types of street-and-road nets. It was
shown that as optimal on the criterion of minimum outage of
clients and service channels may be considered (if one minds
fuel sales only) the structure of two dispensers with all of the
fuels being sold on the station. Outdoor payment terminals
built in dispencers provide at least 10% higher productivity.
During 2000-2014 the model was applied on more than 150
39 Alexey Anatolievitch Bezrodny et al.: Algorithms of Cause-and-effect Approach to Increase Service Net Efficiency
objects.
For gas station nets it was proved that up to 80% of the
modern and perspective flows of clients may be served by
smaller quantity of stations. For various types of street-and-
road nets there were found some characteristics (quantity of
cross-roads, distances between neighboring stations and their
quantity), providing minimal redistribution of clients
bertween objects of the same net for small (up to 500
thousand residents) and medium (up to 1,5 million residents)
cities and towns for non-dominating petroleum supply
company, operating smaller that 25% of a region stations,
and the same on highways.
Figure 4. Interaction of information-logic diagrams and algorithms to improve service nets.
Also in some regions of middle Russia there were
developed optimal structures to serve card clients that
increased sales in volume in 6 times. In these regions and
some CIS countries there were changed technical
maintanance systems that provided cost reduction in 3-15%
at better service. Moreover there were prepared efficient
system structures to serve clients near pumps, security,
automation, procrurement counter-actions, capital
construction, etc. Finally, it was done modelling of
processes on stations that permitted to increase staff
training skills in newly built trainng centers in Saratov and
Volgograd-cities.
5. Conclusion
Service nets are important for an economy and require
their continuous improvement.
A causal-and-effect approach was suggested and new
informational logic diagrams and algorithms were developed.
They are characterized by co-synthesis of controllable and
control systems, descision-making in case of not enough
trustable data from systems and surroundings, possibility to
match objects, processes, events and phenomena of various
nature and so on.
Adequatnes of the methodology is cofirmed by the
proximity of the known and developed models on the similar
feasible regions, reliability of results by statistical data for
more than 15 years of observation, validity of conclusions by
results of approbation and successful multiple applications.
Said above permits to use it for other service nets and
complex system at all.
Conflict of Interest Statement
All the authors do not have any possible conflicts of
interest.
Acknowledgements
I can say as much thanks as possible to my scientific
adviser and teacher Alexandr F. Rezchikov (correspondent-
member of Russian Academy of Sciences) and my First
General Director Semen M. Glozman for all of the so good
things that they have done for me.
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Biography
Alexey Anatolievitch Bezrodny. He graduated from Saratov State University (Physics) in 1995 and RANEPA (Civil
service) in 1999 and participated in the Yeltsin Democracy Fellowship Program (in-Canada training) in 2001-02. In
1995-97 Mr. Bezrodniy worked in Volga R@D Institute in the Field Emission Display Team with SAMSUNG
(Korea). From 1997 he has been working for LUKOIL in Russia, Turkey and Ukraine in engineering, automation,
construction, etc of petroleum supply. He became a candidate of sciences in mathematical modeling in 2004 and a
Doctor of Sciences in System analysis and control in 2011. Since 2015 he works in the LUKOIL central office
(Moscow) being professor at the Gubkin university.
Anatoliy Mikchailovitch Korolenok. In 1977 he graduated from the Bauman Moscow State Technical University,
where he worked as a trainee-investigator. In 1981 he became a candidate of sciences in petroleum engineering and
worked as a senior engineer, researcher and professor at the Gubkin university. Mr. Korolenok became a doctor of
sciences in 1999, from 2003 he is the Dean of the Faculty of Design, Construction and Exploitation of Pipeline
transport systems. Anatoliy M. Korolenok trained 12 candidates and 2 doctors of sciences, he is the author of 130+
articles and books, member of five scientific journal editorial boards, three thesis councils (a chairman in one) and
two technical ones. He is an honorary worker of Russia petroleum and High professional education spheres and
Russian government prize winner in education.
Aleksandr Fedorovitch Rezchikov. In 1977 he graduated from the Samara industrial institute (automation). In 1961-
64 Mr. Rezchikov worked in a factory in Saratov-city, in 1967 he post graduated Saratov Polytechnic Institute, where
continued as a researcher, professor and chief of a Department. In 1968 he became a candidate of sciences in electro-
mechanics, in 1987 – a doctor of sciences defended his thesis in the V.A. Trapeznikov Institute of Control Sciences
(ICS), in 2003 - was elected as a correspondent-member of the Russian Academy of Sciences. During 1970-80-s
Aleksandr A. Rezchikov was scientific secretary of Volga region committee of USSR Academy of Sciences and
Chief of Economics Department of Saratov region political administration. In 1987 he became director of the Institute
of Precision Mechanics and Control of the USSR Academy of Sciences whom he was till 2016 when he continued his
activity in ICS in Moscow. Mr. Rezchikov created the scientific school of complex system control, trained 21 candidates and 6 doctors of
sciences, the author 200+ articles and books, member of three scientific journal editorial boards, a honorary worker of science of Russian
Federation, etc.